1_getting_started.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/araffin/rl-tutorial-jnrr19/blob/master/1_getting_started.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hyyN-2qyK_T2",
        "colab_type": "text"
      },
      "source": [
        "# Stable Baselines Tutorial - Getting Started\n",
        "\n",
        "Github repo: https://github.com/araffin/rl-tutorial-jnrr19\n",
        "\n",
        "Stable-Baselines: https://github.com/hill-a/stable-baselines\n",
        "\n",
        "Documentation: https://stable-baselines.readthedocs.io/en/master/\n",
        "\n",
        "RL Baselines zoo: https://github.com/araffin/rl-baselines-zoo\n",
        "\n",
        "Medium article: [https://medium.com/@araffin/stable-baselines-a-fork-of-openai-baselines-df87c4b2fc82](https://medium.com/@araffin/stable-baselines-a-fork-of-openai-baselines-df87c4b2fc82)\n",
        "\n",
        "[RL Baselines Zoo](https://github.com/araffin/rl-baselines-zoo) is a collection of pre-trained Reinforcement Learning agents using Stable-Baselines.\n",
        "\n",
        "It also provides basic scripts for training, evaluating agents, tuning hyperparameters and recording videos.\n",
        "\n",
        "\n",
        "## Introduction\n",
        "\n",
        "In this notebook, you will learn the basics for using stable baselines library: how to create a RL model, train it and evaluate it. Because all algorithms share the same interface, we will see how simple it is to switch from one algorithm to another.\n",
        "\n",
        "\n",
        "## Install Dependencies and Stable Baselines Using Pip\n",
        "\n",
        "List of full dependencies can be found in the [README](https://github.com/hill-a/stable-baselines).\n",
        "\n",
        "```\n",
        "\n",
        "sudo apt-get update && sudo apt-get install cmake libopenmpi-dev zlib1g-dev\n",
        "```\n",
        "\n",
        "\n",
        "```\n",
        "\n",
        "pip install stable-baselines[mpi]\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gWskDE2c9WoN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!apt-get install ffmpeg freeglut3-dev xvfb  # For visualization\n",
        "!pip install stable-baselines[mpi]==2.8.0\n",
        "# Stable Baselines only supports tensorflow 1.x for now\n",
        "%tensorflow_version 1.x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FtY8FhliLsGm",
        "colab_type": "text"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gcX8hEcaUpR0",
        "colab_type": "text"
      },
      "source": [
        "Stable-Baselines works on environments that follow the [gym interface](https://stable-baselines.readthedocs.io/en/master/guide/custom_env.html).\n",
        "You can find a list of available environment [here](https://gym.openai.com/envs/#classic_control).\n",
        "\n",
        "It is also recommended to check the [source code](https://github.com/openai/gym) to learn more about the observation and action space of each env, as gym does not have a proper documentation.\n",
        "Not all algorithms can work with all action spaces, you can find more in this [recap table](https://stable-baselines.readthedocs.io/en/master/guide/algos.html)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BIedd7Pz9sOs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import gym\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ae32CtgzTG3R",
        "colab_type": "text"
      },
      "source": [
        "The first thing you need to import is the RL model, check the documentation to know what you can use on which problem"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R7tKaBFrTR0a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from stable_baselines import PPO2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-0_8OQbOTTNT",
        "colab_type": "text"
      },
      "source": [
        "The next thing you need to import is the policy class that will be used to create the networks (for the policy/value functions).\n",
        "This step is optional as you can directly use strings in the constructor: \n",
        "\n",
        "```PPO2('MlpPolicy', env)``` instead of ```PPO2(MlpPolicy, env)```\n",
        "\n",
        "Note that some algorithms like `SAC` have their own `MlpPolicy` (different from `stable_baselines.common.policies.MlPolicy`), that's why using string for the policy is the recommened option."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ROUJr675TT01",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from stable_baselines.common.policies import MlpPolicy"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nu9_iF4dUTcr",
        "colab_type": "text"
      },
      "source": [
        "Finally, we will import a VecEnv that is required for PPO2. This will be useful later when doing multiprocessing. More on that later..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UCEU9lBRUUe1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from stable_baselines.common.vec_env import DummyVecEnv"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RapkYvTXL7Cd",
        "colab_type": "text"
      },
      "source": [
        "## Create the Gym env and instantiate the agent\n",
        "\n",
        "For this example, we will use CartPole environment, a classic control problem.\n",
        "\n",
        "\"A pole is attached by an un-actuated joint to a cart, which moves along a frictionless track. The system is controlled by applying a force of +1 or -1 to the cart. The pendulum starts upright, and the goal is to prevent it from falling over. A reward of +1 is provided for every timestep that the pole remains upright. \"\n",
        "\n",
        "Cartpole environment: [https://gym.openai.com/envs/CartPole-v1/](https://gym.openai.com/envs/CartPole-v1/)\n",
        "\n",
        "![Cartpole](https://cdn-images-1.medium.com/max/1143/1*h4WTQNVIsvMXJTCpXm_TAw.gif)\n",
        "\n",
        "Note: vectorized environments allow to easily multiprocess training. In this example, we are using only one process, hence the DummyVecEnv.\n",
        "\n",
        "We chose the MlpPolicy because input of CartPole is a feature vector, not images.\n",
        "\n",
        "The type of action to use (discrete/continuous) will be automatically deduced from the environment action space\n",
        "\n",
        "\n",
        "Here we are using the [Proximal Policy Optimization](https://stable-baselines.readthedocs.io/en/master/modules/ppo2.html) algorithm (PPO2 is the version optimized for GPU), which is an Actor-Critic method: it uses a value function to improve the policy gradient descent (by reducing the variance).\n",
        "\n",
        "It combines ideas from [A2C](https://stable-baselines.readthedocs.io/en/master/modules/a2c.html) (having multiple workers and using an entropy bonus for exploration) and [TRPO](https://stable-baselines.readthedocs.io/en/master/modules/trpo.html) (it uses a trust region to improve stability and avoid catastrophic drops in performance).\n",
        "\n",
        "PPO is an on-policy algorithm, which means that the trajectories used to update the networks must be collected using the latest policy.\n",
        "It is usually less sample efficient than off-policy alorithms like [DQN](https://stable-baselines.readthedocs.io/en/master/modules/dqn.html), [SAC](https://stable-baselines.readthedocs.io/en/master/modules/sac.html) or [TD3](https://stable-baselines.readthedocs.io/en/master/modules/td3.html), but is much faster regarding wall-clock time.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pUWGZp3i9wyf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "env = gym.make('CartPole-v1')\n",
        "# vectorized environments allow to easily multiprocess training\n",
        "# we demonstrate its usefulness in the next examples\n",
        "env = DummyVecEnv([lambda: env])  # The algorithms require a vectorized environment to run\n",
        "\n",
        "model = PPO2(MlpPolicy, env, verbose=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4efFdrQ7MBvl",
        "colab_type": "text"
      },
      "source": [
        "We create a helper function to evaluate the agent:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "63M8mSKR-6Zt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def evaluate(model, num_episodes=100):\n",
        "    \"\"\"\n",
        "    Evaluate a RL agent\n",
        "    :param model: (BaseRLModel object) the RL Agent\n",
        "    :param num_episodes: (int) number of episodes to evaluate it\n",
        "    :return: (float) Mean reward for the last num_episodes\n",
        "    \"\"\"\n",
        "    # This function will only work for a single Environment\n",
        "    env = model.get_env()\n",
        "    all_episode_rewards = []\n",
        "    for i in range(num_episodes):\n",
        "        episode_rewards = []\n",
        "        done = False\n",
        "        obs = env.reset()\n",
        "        while not done:\n",
        "            # _states are only useful when using LSTM policies\n",
        "            action, _states = model.predict(obs)\n",
        "            # here, action, rewards and dones are arrays\n",
        "            # because we are using vectorized env\n",
        "            obs, reward, done, info = env.step(action)\n",
        "            episode_rewards.append(reward)\n",
        "\n",
        "        all_episode_rewards.append(sum(episode_rewards))\n",
        "\n",
        "    mean_episode_reward = np.mean(all_episode_rewards)\n",
        "    print(\"Mean reward:\", mean_episode_reward, \"Num episodes:\", num_episodes)\n",
        "\n",
        "    return mean_episode_reward"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zjEVOIY8NVeK",
        "colab_type": "text"
      },
      "source": [
        "Let's evaluate the un-trained agent, this should be a random agent."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xDHLMA6NFk95",
        "colab_type": "code",
        "outputId": "d5039fc4-8aa4-4e7c-a770-1be311fd106a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Random Agent, before training\n",
        "mean_reward_before_train = evaluate(model, num_episodes=100)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean reward: 22.25 Num episodes: 100\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r5UoXTZPNdFE",
        "colab_type": "text"
      },
      "source": [
        "## Train the agent and evaluate it"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e4cfSXIB-pTF",
        "colab_type": "code",
        "outputId": "d861ecf1-3986-4f36-c078-664d5ba82ea8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Train the agent for 10000 steps\n",
        "model.learn(total_timesteps=10000)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<stable_baselines.ppo2.ppo2.PPO2 at 0x7f45e075f7f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ygl_gVmV_QP7",
        "colab_type": "code",
        "outputId": "8de61bb2-a0e7-42ac-bac8-993535ea1b34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Evaluate the trained agent\n",
        "mean_reward = evaluate(model, num_episodes=100)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean reward: 156.24 Num episodes: 100\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A00W6yY3NkHG",
        "colab_type": "text"
      },
      "source": [
        "Apparently the training went well, the mean reward increased a lot ! "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xVm9QPNVwKXN",
        "colab_type": "text"
      },
      "source": [
        "### Prepare video recording"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MPyfQxD5z26J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Set up fake display; otherwise rendering will fail\n",
        "import os\n",
        "os.system(\"Xvfb :1 -screen 0 1024x768x24 &\")\n",
        "os.environ['DISPLAY'] = ':1'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HS1VBBaQ_emT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install pyglet==1.3.1  # pyglet v1.4.1 throws an error"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SLzXxO8VMD6N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import base64\n",
        "from pathlib import Path\n",
        "\n",
        "from IPython import display as ipythondisplay\n",
        "\n",
        "def show_videos(video_path='', prefix=''):\n",
        "  \"\"\"\n",
        "  Taken from https://github.com/eleurent/highway-env\n",
        "\n",
        "  :param video_path: (str) Path to the folder containing videos\n",
        "  :param prefix: (str) Filter the video, showing only the only starting with this prefix\n",
        "  \"\"\"\n",
        "  html = []\n",
        "  for mp4 in Path(video_path).glob(\"{}*.mp4\".format(prefix)):\n",
        "      video_b64 = base64.b64encode(mp4.read_bytes())\n",
        "      html.append('''<video alt=\"{}\" autoplay \n",
        "                    loop controls style=\"height: 400px;\">\n",
        "                    <source src=\"data:video/mp4;base64,{}\" type=\"video/mp4\" />\n",
        "                </video>'''.format(mp4, video_b64.decode('ascii')))\n",
        "  ipythondisplay.display(ipythondisplay.HTML(data=\"<br>\".join(html)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LTRNUfulOGaF",
        "colab_type": "text"
      },
      "source": [
        "We will record a video using the [VecVideoRecorder](https://stable-baselines.readthedocs.io/en/master/guide/vec_envs.html#vecvideorecorder) wrapper, you will learn about those wrapper in the next notebook."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Trag9dQpOIhx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from stable_baselines.common.vec_env import VecVideoRecorder\n",
        "\n",
        "def record_video(env_id, model, video_length=500, prefix='', video_folder='videos/'):\n",
        "  \"\"\"\n",
        "  :param env_id: (str)\n",
        "  :param model: (RL model)\n",
        "  :param video_length: (int)\n",
        "  :param prefix: (str)\n",
        "  :param video_folder: (str)\n",
        "  \"\"\"\n",
        "  eval_env = DummyVecEnv([lambda: gym.make('CartPole-v1')])\n",
        "  # Start the video at step=0 and record 500 steps\n",
        "  eval_env = VecVideoRecorder(env, video_folder=video_folder,\n",
        "                              record_video_trigger=lambda step: step == 0, video_length=video_length,\n",
        "                              name_prefix=prefix)\n",
        "\n",
        "  obs = eval_env.reset()\n",
        "  for _ in range(video_length):\n",
        "    action, _ = model.predict(obs)\n",
        "    obs, _, _, _ = eval_env.step(action)\n",
        "\n",
        "  # Close the video recorder\n",
        "  eval_env.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KOObbeu5MMlR",
        "colab_type": "text"
      },
      "source": [
        "### Visualize trained agent\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iATu7AiyMQW2",
        "colab_type": "code",
        "outputId": "45feec21-11b5-4aea-c580-2993fc3d2b81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "record_video('CartPole-v1', model, video_length=500, prefix='ppo2-cartpole')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saving video to  /content/videos/ppo2-cartpole-step-0-to-step-500.mp4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-n4i-fW3NojZ",
        "colab_type": "code",
        "outputId": "345d869b-3bc6-48eb-86d2-949fadf5d4fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        }
      },
      "source": [
        "show_videos('videos', prefix='ppo2')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<video alt=\"videos/ppo2-cartpole-step-0-to-step-500.mp4\" autoplay \n",
              "                    loop controls style=\"height: 400px;\">\n",
              "                    <source 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type=\"video/mp4\" />\n",
              "                </video>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Y8zg4V566qD",
        "colab_type": "text"
      },
      "source": [
        "## Bonus: Train a RL Model in One Line\n",
        "\n",
        "The policy class to use will be inferred and the environment will be automatically created. This works because both are [registered](https://stable-baselines.readthedocs.io/en/master/guide/quickstart.html)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iaOPfOrwWEP4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = PPO2('MlpPolicy', \"CartPole-v1\", verbose=1).learn(1000)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Se8SHBN17Fy4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WBf84jU05mgi",
        "colab_type": "text"
      },
      "source": [
        "## Train a DQN agent\n",
        "\n",
        "In the previous example, we have used PPO, which one of the many algorithms provided by stable-baselines.\n",
        "\n",
        "In the next example, we are going train a [Deep Q-Network agent (DQN)](https://stable-baselines.readthedocs.io/en/master/modules/dqn.html), and try to see possible improvements provided by its extensions (Double-DQN, Dueling-DQN, Prioritized Experience Replay).\n",
        "\n",
        "The essential point of this section is to show you how simple it is to tweak hyperparameters.\n",
        "\n",
        "The main advantage of stable-baselines is that it provides a common interface to use the algorithms, so the code will be quite similar.\n",
        "\n",
        "\n",
        "DQN paper: https://arxiv.org/abs/1312.5602\n",
        "\n",
        "Dueling DQN: https://arxiv.org/abs/1511.06581\n",
        "\n",
        "Double-Q Learning: https://arxiv.org/abs/1509.06461\n",
        "\n",
        "Prioritized Experience Replay: https://arxiv.org/abs/1511.05952"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kikNl-tT92Ae",
        "colab_type": "text"
      },
      "source": [
        "### Vanilla DQN: DQN without extensions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tdLdYABz8TJx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Same as before we instantiate the agent along with the environment\n",
        "from stable_baselines import DQN\n",
        "\n",
        "# Deactivate all the DQN extensions to have the original version\n",
        "# In practice, it is recommend to have them activated\n",
        "kwargs = {'double_q': False, 'prioritized_replay': False, 'policy_kwargs': dict(dueling=False)}\n",
        "\n",
        "# Note that the MlpPolicy of DQN is different from the one of PPO\n",
        "# but stable-baselines handles that automatically if you pass a string\n",
        "dqn_model = DQN('MlpPolicy', 'CartPole-v1', verbose=1, **kwargs)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E4A_IOZ_9fzW",
        "colab_type": "code",
        "outputId": "b682bbf9-5de9-40d2-f9ff-b031598c5dd2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Random Agent, before training\n",
        "mean_reward_before_train = evaluate(dqn_model, num_episodes=100)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean reward: 9.29 Num episodes: 100\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4wbuvAKk9spH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Train the agent for 10000 steps\n",
        "dqn_model.learn(total_timesteps=10000, log_interval=10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QDQTdpYv9xJN",
        "colab_type": "code",
        "outputId": "363f5ed3-5bb1-4ce2-e218-10018818ecc9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Evaluate the trained agent\n",
        "mean_reward = evaluate(dqn_model, num_episodes=100)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean reward: 130.02 Num episodes: 100\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GFJvqsMl96l7",
        "colab_type": "text"
      },
      "source": [
        "### DQN + Prioritized Replay"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "roCSjGu69-lA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Activate only the prioritized replay\n",
        "kwargs = {'double_q': False, 'prioritized_replay': True, 'policy_kwargs': dict(dueling=False)}\n",
        "\n",
        "dqn_per_model = DQN('MlpPolicy', 'CartPole-v1', verbose=1, **kwargs)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DLnLhos5-lRm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dqn_per_model.learn(total_timesteps=10000, log_interval=10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xQjF5S_g-mFN",
        "colab_type": "code",
        "outputId": "4ce14e23-8f2b-40c1-90e1-b333d36f6997",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Evaluate the trained agent\n",
        "mean_reward = evaluate(dqn_per_model, num_episodes=100)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean reward: 110.18 Num episodes: 100\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Skny8MUN9_Ky",
        "colab_type": "text"
      },
      "source": [
        "### DQN + Prioritized Experience Replay + Double Q-Learning + Dueling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fDCys-Vg-yYR",
        "colab_type": "code",
        "outputId": "a3bc7511-d247-47ca-952e-53e95a1597a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Activate all extensions\n",
        "kwargs = {'double_q': True, 'prioritized_replay': True, 'policy_kwargs': dict(dueling=True)}\n",
        "\n",
        "dqn_full_model = DQN('MlpPolicy', 'CartPole-v1', verbose=1, **kwargs)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Creating environment from the given name, wrapped in a DummyVecEnv.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "koHGB-VN-81O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dqn_full_model.learn(total_timesteps=10000, log_interval=10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6cHRULdp--nN",
        "colab_type": "code",
        "outputId": "5b2a3471-f6fe-4594-8f91-e4c62505a07a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "mean_reward = evaluate(dqn_per_model, num_episodes=100)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean reward: 110.02 Num episodes: 100\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n4Q9dR3UC5Zb",
        "colab_type": "text"
      },
      "source": [
        "In this particular example, the extensions does not seem to give any improvement compared to the simple DQN version.\n",
        "They are several reasons for that:\n",
        "\n",
        "1. `CartPole-v1` is a pretty simple environment\n",
        "2. We trained DQN for very few timesteps, not enough to see any difference\n",
        "3. The default hyperparameters for DQN are tuned for atari games, where the number of training timesteps is much larger (10^6) and input observations are images\n",
        "4. We have only compared one random seed per experiment"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FrI6f5fWnzp-",
        "colab_type": "text"
      },
      "source": [
        "## Conclusion\n",
        "\n",
        "In this notebook we have seen:\n",
        "- how to define and train a RL model using stable baselines, it takes only one line of code ;)\n",
        "- how to use different RL algorithms and change some hyperparameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "73ji3gbNDkf7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}